摘要
【目的】为提高产品选择效率,帮助消费者更好地制定购物决策,本文在门限递归单元的基础上,提出一种特征强化双向门限递归单元模型(Feature Bidirectional Gated Recurrent Unit,F-Bi GRU)。【方法】首先,获取相关产品的在线评论信息;然后对在线评论按照产品属性进行分割;使用正向情感评论和负向情感评论对F-Bi GRU模型进行训练;最后使用F-Bi GRU模型对产品各属性的评论进行情感量化,得到产品各属性的情感满意程度,并使用TOPSIS法对候选产品进行排序。【结果】选取汽车口碑文本评论数据进行实证,对比相关情感分析方法,F-Bi GRU方法提高了情感分析的准确度,更适应在线评论短文本的特点。【局限】深度学习模型需要大规模的数据集,本文方法在一些小数据集上的表现可能不佳。【结论】基于F-Bi GRU情感分析的产品选择方法提高了情感分析的准确度,能更高效快捷地帮助消费者进行产品选择。
[Objective] This paper proposes a product selection method based on the Feature Bidirectional Gated Recurrent Unit model （F-BiGRU）, aiming to improve the efficiency of customers＇ product selection and help them make better shopping decisions. ]Methods] First, we retrieved online reviews for related products. Then, we categorized these online reviews in accordance with the product attributes. Third, we trained the F-BiGRU model using positive and negative reviews. Fourth, we quantified the sentiment of reviews on different attributes with the F-BiGRU model. Finally, we got the degrees of satisfaction on product attributes, and sorted the products using TOPSIS method. [Results] We retrieved the review texts on cars to conduct an empirical analysis. We found that the F-BiGRU method improved the accuracy of sentiment analysis, and is more appropriate for the short text reviews than traditional methods [Limitations] The proposed deep learning model requires large dataset, which limits its performance with smaller datasets. [Conclusions] The product selection method based on F-BiGRU helps consumers choose needed products more efficiently.
作者
余本功
张培行
许庆堂
Yu Bengong;Zhang Peihang;Xu Qingtang(School of Management,Hefei University of Technology,Hefei 230009,Chin;Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2018年第9期22-30,共9页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目"基于制造大数据的产品研发知识集成与服务机制研究"(项目编号:71671057)的研究成果之一
关键词
产品选择
在线评论
情感分析
深度学习
门限递归单元
Product Selection
Online Review
Sentiment Analysis
Deep Learning
Gated Recurrent Unit
